A Novel Difference Expansion Transform for Reversible Data Embedding

TLDR

Reversible data embedding theory has ushered in a new era for data hiding and information security, with the difference expansion transform emerging as a breakthrough scheme. The study demonstrates that a simplified location map and new expandability in the difference expansion method can increase embedding capacity without increasing distortion. The scheme exploits the quasi‑Laplace distribution of difference values to enable reversible embedding while ensuring full restoration of both original and embedded data. The new method attains higher embedding capacity than prior difference‑expansion schemes, yet preserves the low distortion characteristic of the original approach.

Abstract

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.